mgo.licio.us

"The face of the operation is Briatore (referred to exclusively in the film by his colleagues and angry, chanting detractors as "Flavio"), an anthropomorphic radish who spends most of his time at QPR plotting to fire all of the managers."

At press time, Harbaugh had sent Michigan’s athletic department an envelope containing a heavily annotated seating chart, a list of the 63,000 seat views he had found unsatisfactory, and a glowing 70-page report on section 25, row 12, seat 9, which he claimed is “exactly what the great sport of football is all about.”

I figure I should get this in now before the annual Brian Cook killer content tsunami hits next week (so stoked). This is an exercise if have done publically twice now (2010, 2013) and its turning out to be a worthwhile thing to do. I think last years results are about as good as can be expected and suspect that it will be difficult to match them again. That’s not going to stop me from trying though.

Here are a few process/assessment notes to help you judge where I’m coming from on these. As with all fortune tellers I try to give myself as much wiggle room as I think I need. I think I can generally get the tier correct even if the number is off somewhat.

Getting within 4 points of the actual value is good enough for me to call it a bingo. In essence I'm claiming an error of +/- 4 rating points. That’s kinds of a wide berth but I think seeking for more accuracy than that is a fool’s errand.

In cases were I see the potential for high variation, I post a range (sometimes narrow, sometimes wide). This is partially to help me list the guys in the order I think they'll land and partially to maximize my opportunity to be right. If the actual result goes through the window I claim it as “on the money”, otherwise I use the closest goal post to conduct the assessment. This is about identifying potential and likely threats.

I try to be as positive as I can about these assessments. It is my nature to be optimistic and look for the ways good outcomes might manifest. If the stated range is below or contains 130, that's my polite way of saying that I think the player might post a poor performance. I'm not going to predict a dude is going to suck because that's a shitty thing to do. If the predicted range is below/contains 130 and the player end up below that, I count it as a bingo: I thought he would struggle and he did.

If the player listed gets beat out for the starting gig, the assessment transfers to the new player. I try to figure out either who I think should be the starter or who will be. If I get that wrong then so be it and bad on me for not teasing it out. Plus, it should avoidable by waiting to post closer to the season.

I try not to pop my collar this hard but I'm very pleased with the results from last year. The chart below is a brief tabulated review of what was said and what played out.

PLAYER

ASSESSMENT

PRED

REAL

Rob Henry Purdue

"…there’s regime change in West Lafayette and the Boilermakers only have 5 starters returning on offense. ...will do well to post a 125."

125

116.1

Philip Nelson Minnesota

"It wouldn’t be a shock if he jumped up to the low 130 range but that would be a neat trick….125-135"

"I’ll wager that it takes a year for [the new offense] to hum and look for Stave to slide a little to the 140 range"

140

138.1

Taylor Martinez Nebraska

"This will be his fourth year as starter and at this point he has leveled off at the seasoned veteran level for a passer."

140

140

Nathan Scheelhasse Illinois

"I expect Nate to return to his 130 form."

130

140.7

Cameron Coffman Indiana

"It’s possible either [Coffman or Sudfeld] will be the guy this coming season but I’m going to assume Coffman’s experience gives him the nod. 130 – 140"

140

142

Devin Gardner Michigan

paraphrasing: potentially a monster but will most likely fall from the monster category to the really friggin good category.

145

146

Kain Colter Northwestern

"I view Kain’s rating as stable and unfortunately can’t see him doing more than a 130 … I think he’s better than that but the numbers don't lie."

130

148.3

Braxton Miller Ohio State

"The YPA and TD% are where the magic will happen for OSU...If those numbers improve, then Braxton will keep folks up at night. I suspect they will. 145 - 160"

160

158.1

Six of the twelve ratings predictions were very close to the actual value. I would never had guessed it could be this high. Either this stuff is more predictable than I ever would have imagined or The KNOWLEDGE has taken over my computer. Of the 12 Big Ten QB’s assessed only two 2 broke out of the expected tiers: Nathan Scheelhasse and Kain Colter.

Scheelhasse gets a tip of the cap for defying the numbers and pulling off the Stanzi Leap even though he had to overcome scheme and support issues. Colter’s case was a flat out miss. I stated that I felt like he was better than I could justify with the data; that was wrong he had previously performed at the monster level in 2011 but his roll expanded in 2012 and I assumed it would continue to expand and therefore continue to reduce the oops-pow-surpriseness of using him as a changeup. It didn't and the change-up nature of his role along with his skill set allowed him to be a part time monster.

But, Tommy Rees was my pièce de résistance:

Looking forward, maybe Tommy finally says [eff] it and let’s it rip a bit in his last go around. To me that looks like Tommy Reese 2011 with fewer Interceptions. That means 135 –140, probably. … Otherwise, he is what he is: 130.

One more thing, this year I’m trying to account for schedule strength both in retrospect as well as looking forward. Retrospect is easy, I’m just looking at Football Outsider’s 2013 Passing Defense S&P+ and looking at how many defenses were easy (bottom 30), hard (top 30), and in between. Very arbitrary, but its better than nothing.

Looking forward I have taken 2013 final rankings and looked at the number of returning starters as well as returning defensive production (percentage of tackles returning) to get an idea of where I think teams are likely to end up. I also bake in mean regression in the sense that if you’re #1, you’re not likely to be that again even if you remain good and if you’re terrible regression should pull you up. It’s all kinda vague and this diary is already super long so here’s the chart I put together to help me figure out which of the B1G schedules I expect to be QB friendly or not-so-friendly. This chart is forward looking:

/further ado

Non-Conference Opponents

Kam Bryant, Appalachian State

2013 Rating: 151.1

CMP%

YPA

TD%

INT%

Expected Values

0.638

8.29

0.074

0.019

Actual Values

0.712

8.15

0.042

0.012

Single Factor Rating

183.6

148.2

116.6

221.9

Eh boy…Kam Bryant was kind of good last year. And, he actually improved his completion percentage from the previous year. Sure, sure, FCS, but you still have to make the ball go where you want it to. They had a lot of returning players last year and I can’t figure out why they lost so many games. My guess is bad defense and the fact that they we in the first year of a coaching transition. This year they once again have a lot of experience returning on offense including all 5 offensive lineman with 126 career starts among them. So, like, good QB, veteran team, um, uh…eh boy. Its good that we like our defense this year.

Projection: too many unknowns

Everett Golson, Notre Dame

2013 Rating: 131

CMP%

YPA

TD%

INT%

Expected Values

0.592

7.26

0.058

0.027

Actual Values

0.588

7.56

0.038

0.019

Single Factor Rating

129.8

136.8

111.0

185.9

Obviously, having Golson return is good but he wasn’t that good of a passer in 2012. Remember, Notre Dame’s defense was stellar that season and Goslon could bail himself out with his legs. I think the passer rating factors prove it: low accuracy, meh YPA, low TD rate, awesome INT rate. The TD and INT rates are what they are because Golson would simply pull the ball down and run rather than force the ball into a bad spot. Smart.

However, I wouldn't say he’s a a scary runner either judging by his rushing stats from that season. Sure, he can do some things but we’re not talking about Johnny Football here. He’s two off-seasons removed from that performance and I expect his skill level to be much improved. Nothing to do but work on technique. Yeah man, he should be pretty good.

The scandal type substance going on down in South Bend damages the defensive roster for the most part. Otherwise, ND has some to replace 2 starters on the offensive line and new primary receivers. Notre Dame has recruited very well under Kelly so I don't expect them to have a problem finding the answers.

Projection: 140

Andrew Hendrix, Miami (OH)

Hendrix was an ESPN four-star prospect in the class of 2010 and was simply stuck behind Everett Golson and Tommy Rees the whole time. Realistically, last year would have been his first real chance to start and though Rees wasn’t a stellar QB he was a solid one. Chuck Martin, Miami's new head coach, the offensive coordinator and quarterbacks coach at Notre Dame the last two seasons and actually worked for an eventually replaced Brian Kelly at Grand Valley. This is a pretty good situation, in that regard. Unfortunately, Miami has a new head coach for a reason, they stunk the last 3 years and were particularly bad last year. Their offensive line is all upper classmen but have very little starting experience between them.All told, I think Miami can have a decent offense this year and Hendrix should do well.

Projection: 135 – 140.

Travis Wilson, Utah

2013 Rating: 129.6

CMP%

YPA

TD%

INT%

Expected Values

0.589

7.19

0.056

0.028

Actual Values

0.561

7.71

0.068

0.068

Single Factor Rating

118.1

139.6

149.5

50.8

Wilson played and started in nine games last year, leading Utah to a 4-2 start, including an upset win over Stanford. But then he hurt his throwing hand and his season ended after suffering a concussion against Arizona State. Wilson also played in 12 games in 2012 so, this will be his third year as a starter. Utah offensive line will be young on the right side and but returns 3 players who are now upper classmen. Their leading receiver from last year (Dres Anderson) is back as is their leading rusher (Bubba Poole) but Poole doesn't look like a dynamic runner to me.Wilson should be decent.

Projection: 135

Projected B1G Rankings

Danny Etling, Purdue

2013 Rating: 116.1

CMP%

YPA

TD%

INT%

Expected Values

0.558

6.50

0.046

0.033

Actual Values

0.558

6.33

0.037

0.026

Single Factor Rating

116.8

112.6

110.7

147.4

True freshman Etling generally played to his rating during his first year as a starter and with poor support around him and a new coaching staff. On top of that Purdue's schedule was light on the cupcakes—probably because they didn't play themselves (zing!)—yet they played the normal amount of good and manageable teams. So the deck was stacked way against Etling last year and that is also reflected in his rating. That said, his INT% was very good which bodes well for his decision making.

Etling will naturally improve as a second year player and the Boilermakers return experienced skill position players. Unfortunately, they need to break in new starters at 3 locations on the O Line so that's bad for Danny. Also, Akeem Hunt does not look to be a very dynamic runner according to my little RB Rating thing. But, I expect the passing defenses Purdue will be facing to be generally favorable*.

Projection: 125 – 130

Gary Nova, Rutgers

2013 Rating: 124.7

CMP%

YPA

TD%

INT%

Expected Values

0.577

6.94

0.053

0.03

Actual Values

0.545

7.13

0.059

0.046

Single Factor Rating

110.9

128.2

139

93.75

I understand why that Gary Nova anti-hype video exists now… The team/scheme stuff looks OK and RB Paul James looks legit though he missed some time last year with a broken tibia. Also, the schedule Rutgers tilted against last year ended up being pretty soft from a QB's perspective so there's really no excuse - Nova straight up performed poorly last year. Its on Nova and his coaches to improve the efficiency of the passing game.

Unfortunately, Nova is probably maxed out in terms of improvement. Dude is a senior who was a returning starter that had played in 18 games and started 13 going into the 2013 season. If he was going to make a leap, it should have showed up by now. Their offensive line returns plenty of experience, James will tote the rock like a boss, the offense returns 9 starters, and the defense returns a lot of production. Unfortunately, I think Nova is what he's going to be: a mediocre QB. For the record, I said similar things about Ricky Stanzi going into 2011 and he threw an egg at my face.

Projection: 130-135

Mitch Leidner, Minnesota

2013 Rating: 131.9

CMP%

YPA

TD%

INT%

Expected Values

0.594

7.31

0.058

0.027

Actual Values

0.551

7.94

0.038

0.013

Single Factor Rating

113.8

144.1

112.0

217.6

Mitch no longer has to worry about competing for the starting spot after Phillip Nelson transferred in the offseason. This should allow him to focus on learning the offense and improving his game. Unfortunately there’s not a whole lot to base a projection on other than his recruiting profile and Kill’s track record for developing QBs. The offensive line for the gophers returns a lot of experience and RBs Rodrick Williams Jr. and David Cobb should both be able to contribute to Mitch’s progression. Between the line and the backs, Leidner should find enough time to be okey but his skills are grossly lacking at this point in time.

Projection: 130-135

J.T. Barret, Ohio State

File Not Found, Man. When I find myself in a desert of data I turn to Proxy analysis. I did this to great affect in 2010 when trying to figure out what might be possible out of Denard that year. The thing is, there was *some* data to work with there. We knew he wasn’t a very good passer but that it sounded like he had tangibly improved to the point of being a viable QB. Here, we’ve got nothing. Well, not *nothing*…

We know the style of QB he is (Dual Threat), the he was a well regarded recruit (top 100-ish, 4 star), and that he’ll be playing in a very good offensive system (Urban Meyer). The proxies that I think are reasonable comps are listed in the table below.

The bigger problem here is that Ohio State’s offense just got gutted. With the loss of Braxton Miller they only return 4 starters and have an offensive line that has the same issues as Michigan’s does. I don’t doubt that there’s talent available but getting good at this game requires experience and there’s only one way to do that: play. Their two leading rushers (Miller, Hyde) are gone and though Ezekiel Elliot and Bri’onte Dunn are talented, they’re inexperienced…and so is everybody else! The run game can’t cover for the pass game and the pass game can’t cover for the run game.

In terms of schedule, Ohio State will have to deal with Virginia Tech, Michigan State, and Michigan all of which I project to be very good defenses and they’re light on cupcakes. I think this is the second toughest schedule in the conference behind Maryland.

That’s a bad overall mix, y’all. We are dealing with Ohio State so maybe things come together, but those are headwinds...that’s a daggum hurricane. I’m expecting JT to be in the lower end of his proxy range.

Projection: 130 - 135

Year

Name

Team

QBRat

2012

Brett Hundley

UCLA

147.7

2013

Devin Gardner

MICH

146.1

2013

Nick Marshall

AUB

143.2

2012

Bo Wallace

MISS

142.7

2013

Marquise Williams

NC

141.1

2010

Chris Relf

MSST

141

2012

Braxton Miller

OSU

140.5

2011

James Franklin

Mizz

139.9

2009

B.J. Daniels

USF

139.5

2013

C.J. Brown

MD

135.9

2011

Logan Thomas

VATECH

135.5

2012

Jeff Driskel

FLA

132.2

2010

Nathan Scheelhaase

ILL

132

2009

Jarrett Brown

WVU

130.2

2009

Tate Forcier

MICH

128.1

2009

Zac Lee

NEB

126.9

2011

Collin Klein

KSST

125.6

Trevor Siemian, Northwestern

2013 Rating: 126.4

CMP%

YPA

TD%

INT%

Expected Values

0.581

7.03

0.054

0.029

Actual Values

0.597

7.21

0.037

0.030

Single Factor Rating

133.9

129.9

110.0

126.5

Trevor was the primary QB for his second year last season and his performance was bad though he showed significant improvement over 2012. The only factor truly lagging his rating was TD rate which probably had something to do with Kain Colter’s skill set. The other three factors are right around where you'd expect them to be for a QB with a rating of 126. The offensive line did give up a lot of sacks last year between he and Kain Colter and the loss of Venric Mark as a backfield weapon certainly hurt, but Trayvon Green did just fine as a primary back so the run game must have been OK. I will say that NW's schedule was light on cupcakes last year as they played only 3 teams I would consider to have weak pass defenses where typical B1G schedule features about 5 of those not including FCS teams. So that's a tough draw that might help explain some of the performance problems.

This year Siemian returns for his 3rd year as a primary starter with an offensive line that has a ton of experience on it. Although Venric Mark has moved on, Treyvon Green is a capable back. I think NW's B1G West schedule will be QB friendly and Trevor should put in his best performance yet.

Projection: 135

Tommy Armstrong Jr., Nebraska

2013 Rating: 124.3

CMP%

YPA

TD%

INT%

Expected Values

0.576

6.92

0.052

0.03

Actual Values

0.520

7.37

0.069

0.061

Single Factor Rating

99.85

133.1

151

63.8

Armstrong split time with Ron Kellogg filling in when Taylor Martinez was injured last year. Both Taylor and Kellogg are gone now so Tommy is the man. In regards to his performance, he struggled pretty hard with his his completion percentage and interception rate which are both kind of hideous. His run-to-pass ratio is pretty high but he doesn't look like an ultra dynamic runner either judging by his Rusher Rating. That doesn't sound very ... intimidating. His YPA was solid and his TD% was elite so if he can improve his accuracy and the support/scheme stuff holds, he could do some damage. He was a first year starter that split time last year whereas he's the man now so he could definitely show rapid year-over-year improvement.

We know from Denard Robinson how quickly a player can develop into a devastating weapon in the right system and situation. For Denard the right system was worth 20 or points in passer rating. Tommy is currently under the Mendoza line so gravity is pulling him up and Nebraska's schedule looks workable from a pass defense perspective so, I can see him easily improving his passer rating by 10-15 points or so; 20 points is not out of the question. The problem is that, although Armstrong has good RBs behind him in Ameer Abdullah and Imani Cross, Nebraska needs to replace a lot of experience on the offensive Line. He'll be better, but he's got a ways to go before he's Taylor F. Martinez. I think he can get there, just not sure if he get there this year or next. High variance here.

Projection: 135-140

Jake Rudock, Iowa

2013 Rating: 126.5

CMP%

YPA

TD%

INT%

Expected Values

0.581

7.03

0.054

0.029

Actual Values

0.590

6.89

0.052

0.038

Single Factor Rating

130.5

123.6

129.5

111.2

Ruddock's first year as a starter was...OK relatively speaking. INTs are what done it. His YPA is also pretty weak. The schedule he faced wasn’t particularly difficult either. So, lack of experience really is the number one thing standing out to me. In regards to the support he had, the Gain% by the running backs looks fine and the sacks were low so it looks like the offensive line did their job. Unfortunately, the wrath that AIRBHG hath wrought has left the Iowan Dilithium stores in dire straights and the Hawkeye running attack was a plodding, cloud-of-dust type of game. There is one guy though: Jordan Canzeri. He didn't get a lot of play last year but he looks legit by the numbers. If I were a Hawkeye fan I'd want to see Canzeri’s role expand in a big way.

Getting back to Ruddock, a year of experience and the switch to the B1G West should bode well for him. The OOC slate is QB-licious and, the way I see it, the top tier B1G pass defenses are in the B1G East. Iowa returns a decent amount of experience on the offensive line and Canzeri is at least available, whether or not he's the guy remains to be seen. With Iowa's defense needing to reload a bit, this could be a breakout year for Ruddock.

Projection: 135

Christian Hackenberg, Penn State

2013 Rating: 134

CMP%

YPA

TD%

INT%

Expected Values

0.599

7.42

0.06

0.026

Actual Values

0.5893

7.538

0.051

0.026

Single Factor Rating

130.36

136.3

128.2

151.1

That was a solid true freshman campaign out of Hack last year: *nice* INT rate with all other factors being where they should have been. He was missing a dynamic running threat but throwing to Allen Robinson is a nice outlet to have. Here again it looks to me like the offensive line did their job just fine in terms of Gain% and Sacks so his biggest hurdle was probably straight up experience. He has that now.

Unfortunately, what he doesn't have any more is Allen Robinson, Bill O'Brien, and an experienced OL. Learning a new system isn't easy no matter how talented you are. Then sprinkle in the schedule: Penn State will have to deal with Michigan State and Michigan in the B1G East. And, oh yeah, UCF's pass D wasn't so bad either last year and they're returning a lot of experience and production. On the plus side there are also some pretty soft pass defenses on there, too.

If I'm Hack, I want Akeel Lynch to by my main backfield weapon as he's the most dynamic runner Penn State has as far as I can tell from my shuper shweet command shenter. Regardless, there are very shtiff stiff headwinds blowing in Happy Valley. I'm looking for Hack to improve completion percentage and maintain his touchdown and interception rate but expect to see his YPA go down. Net result: flat passer rating. That said, there’s no way I can drop him any further on this list in good conscience.

Projection: 135-140

Wes Lunt, Illinois

2013 Rating: 137.3

CMP%

YPA

TD%

INT%

Expected Values

0.606

7.58

0.063

0.025

Actual Values

0.618

8.46

0.046

0.053

Single Factor Rating

143.0

154.3

121.4

79.2

Wes Lunt got hosed. He won the starting gig at OKST as a true freshman in 2012 but got injured and his the job so he transferred. He posted a rating of 137 on 131 attempts which is pretty good even when you discount the lesser competition he faced. The TD Rate and INT rate weren't up to par, but that's typical of a first year starter. Having to sit out 2013 after transferring, he's had the opportunity to absorb Bill Cubits offense from the sideline which should help him get on plane faster once he sees the field. Illinois returns 4 of its OL and a pretty good RB in Josh Ferguson, but they need to replace their best receiving option. The schedule difficulty for 2014 is fine from a QB's perspective.

Projection: 135

Connor Cook, Michigan State

2013 Rating: 135.5

CMP%

YPA

TD%

INT%

Expected Values

0.602

7.49

0.061

0.026

Actual Values

0.587

7.25

0.058

0.016

Single Factor Rating

129.3

130.7

137.0

202.1

Connor Cook is getting a lot of love this off season and why not, my man has a Rose Bowl ring. Forever and ever. But the defense got him that ring; all he had to do is not screw things up. That's my take on the situation. I will say that Cook's INT rate is outstanding but given that his completion percentage was just okay I think that's more a product of a conservative offensive game plan than the residue of well honed skill. There’s just no way to reasonably expect him to be able to repeat that INT rate. And, since he didn't have to play against his own team, the schedule he faced was easier than most of his interleague peers. Then there's Jeremy Langford who is a solid back to hand off to, so... solid initial season but that's all as far as I'm concerned. His rating is probably inflated due to the INT rate.

Looking ahead, he'll have more experience, Langford, probably an expanded playbook, still doesn't have to play vs. MSU, and a pretty normal OL situation so: he should be able to post some nice numbers but I’m not seeing anything better than early Kirk Cousins just yet. That'll do pretty nicely if you ask me.

Projection: 135

Joel Stave, Wisconsin

2013 Rating: 138.1

CMP%

YPA

TD%

INT%

Expected Values

0.608

7.62

0.063

0.024

Actual Values

0.619

7.42

0.065

0.039

Single Factor Rating

143.3

134.0

146.8

108.9

In my write up about Stave last year I indicated that while I liked Stave, I thought his stellar passer rating from 2012 might slide back due to issues with switching offenses in the Bielema departure. It looks like that is exactly what happened. His completion percentage and TD rate both improved but his YPA and INT rate plummeted. The drop in YPA might be attributable to the scheme change but the INT rate is that of a guy who tried to force things to happen.

This year the Badgers return 4 on the OL and Melvin Gordon is definitely the next great Badger running back (where do they find these guys?). The issue he'll have to overcome is the loss of all of his primary pass catchers most notably Jared Abbrederis. The schedule is pretty QB friendly as LSU in week 1 presents the only formidable defense they should see all year. This is another situation where he could get better and not change his rating.

Projection: 140

Nate Sudfeld, Indiana

2013 Rating: 142

CMP%

YPA

TD%

INT%

Expected Values

0.617

7.82

0.066

0.023

Actual Values

0.602

7.84

0.065

0.028

Single Factor Rating

136.1

142.1

146.5

138.3

Sudfled slayed two former incumbents (Roberson, Coffman) to claim to the starting role early last year. That's kind of a big deal as those guys weren't scrubs and Kevin Wilson knows quarterbacks; dude has game. It was his first season with extended starting experience and he put up really good numbers. The skill factors (CMP%, INT%) were slightly low relative to his rating but they were still good. They system/support numbers (YPA, TD%) were great which is exactly what I’d expect from a Kevin Wilson offense targeting Cody Latimer and Kofi Hughes with good QB play. The schedule last year was appropriately challenging as well. Very good performance.

Heading into this year Sudfled returns with a great track record, more experience, all 5 of his offensive linemen, and a dangerous RB in Tevin Coleman. He does have to deal with both Michigan State and Michigan but going up a against tough competition didn't phase this guy last year. I think WR Shane Wynn can step in just fine for Kofi Hughes but the loss of Latimer will hurt the vertical game. I think they find enough answers to stay dangerous.

Projection: 145

C.J. Brown, Maryland

2013 Rating: 135.9

CMP%

YPA

TD%

INT%

Expected Values

0.603

7.51

0.062

0.025

Actual Values

0.589

7.95

0.046

0.025

Single Factor Rating

130.1

144.4

121.8

154.7

I don’t think I like this guy. Not because he's bad but because he might be pretty dang good. I'm thinking about a Kain Colter type of guy that doesn't get taken out of the game for a less dynamic player. In 2011 he started 5 games and was set to be the incumbent in 2012 but he had to take medical redshirt in that year due to a torn ACL he suffered in a non-contact drill during fall camp. Last year was CJ's first full season as a starter and his completion percentage was OK, but his YPA and INT rate were very good; a low TD rate is what held his rating down. Plus, he was a dynamic runner out of the backfield in his first season after ACL surgery. So, like, no thanks. Send this guy back. Oh yeah, he did all that against a tough schedule...Florida State, Virginia Tech, Virginia, Clemson. Two elite pass Ds and to good ones. Now I REALLY don't like him. Nope. No me gusta. Not even un poquito.

It keeps getting worse. Remember Juice Williams? The OC at Illinois calling plays for him is currently in the same capacity at Maryland calling plays for Brown. He's been there 3 years so the system should be well established and the Terp OL is in normal shape. The offense returns 8 starters. Now I really hate him. /Doc Holliday #tombstone

His only issue is the schedule. He has to face 3 probably good pass defenses in Michigan State, Michigan, and Syracuse and no real Illinois-level cupcakes. Don’t sleep on this guy.

Projection: 140 - 150

Devin Gardner, Michigan

2013 Rating: 146.1

CMP%

YPA

TD%

INT%

Expected Values

0.627

8.03

0.070

0.021

Actual Values

0.603

8.58

0.061

0.032

Single Factor Rating

136.3

156.7

140.9

122.6

I’m about to officially become the self-proclaimed Devin Gardner hype man. In fact, I’m going to feed the machine some cash money for a 98 jersey. That’s a good friggin’ jersey. Look at that thing….

News Flash: I ab-so-lu-te-ly L-O-V-E this guy. I would have taken him as the most talented QB in the league even before Braxton Miller sustained his unfortunate injury. That's not a slight to Braxton—dude has game—but that's how much potential I see in Devin. I’ve had him on Monster Watch since last year but blah, blah, offensive line, yadda, yadda, borges, blah. Yeah man, there is *one* known bad, and we cant see how it’ll become a known good. So what? There’s a lot to like about out situation, man:

I'm assessing the schedule as unfriendly to QBs not because I expect many tough games but I don't see as many vulnerable defenses for him to feast on as a typical B1G schedule. That said, there is only one defense that should pose a problem—Michigan State. Whatever I’ll grant them the ability to simply fill the loss of a first round defensive back (Dennard) and a multi-generationally-died-in-the-wool-spartan-baller (Bullough). Big deal, those grow on trees. What? Denicos, Isaiah, Tyler, and Micah are gone too? Psh, ‘Duzzi’s got pockets full of guys better than that. Be warned whereas the practice squad’s just crawling with replacements better than those guys, just you wait. You don't have to believe me this is an acurate statement.

….Otherwise I think it will be a very manageable schedule to say the least.

Devin’s backfield (Green, Smith) frankly haven’t had the opportunity to show their talent because they weren't capable of displacing Fitzgerald Toussaint as starter last year and blah, blah, offensive line, yadda, yadda, blah. But we’ve been worse off going into the season in the very recent past. Even those guys, the offensive line, they’re talent laden (no reason to believe otherwise yet) though still incubating. Do you really want to be around when a god damn baby alien breaks through its shell? Do you? I do, but only because they’re on my side, hoss.

And the weapons, the weapons! The guys Devin will be throwing to are either obnoxious already (Funchess), have shown us real dynamism in the open field (Norfleet), or have observable talent backed up by that sweet, delicious, gloriously unconfirmable-yet-undeniable off-season hype (Darboh, Canteen). Sheeit, I’ll bet on the come no dizzo, all day er’day, son. Scurred money don't make none, holmes. And Chesson, my man, just blowing punk asses up like a neo Lamar “Guns-Don't-Kill-People-I-Do” Woodley.

Yeah, I said it. And it’s too late for take-backs. Lamar. M---a. F---in’. Woodley.

Second and a long two, deep in the fourth quarter at the Spartan thirty-five. Michigan’s down four. The Refs peel away a two tons of flesh and bone and sinew to reveal the ball then quickly scramble to spot it. Several players from both teams sub in and out but most of them are running to the spot jaw-jacking like boys do when they’re about to throw down.

[Is Darboh limping!?! You gotta be f---ing kidding me!]

On his way to the spot, Devin looks towards the sideline and reads the play call, then stops dead in his tracks and swags a little as he rubs his hands together out of anticipation.

[Huh? Oh snap…its about to go down for real].

As he gets to the line of scrimmage, Devin barks out a call to the linemen and does some Bruce Lee ninja nunchaku hand motions for the receivers. The ball is on the right hash and the team settles into Pistol Trips TE with Norfleet, Chesson and Funchess on the line, at the boundry.

[…the f--- is that?]

The line settles for a moment, then Devin motions the TE to the left side of the formation. “Hutt!” The back shows play action before flaring to the left flat ad Norfleet drops into a into the right flat.

“SCREEN!”

Safeties and LBs close down hard on ‘Fleet and the offensive line pushes the defensive tackles and ends play side. Fleet takes a hard jab step forward then drops his left foot towards the feild.

[huh? oh sh---]

“DOUBLE P---”

Before Dantonio can say ass, Fleet hurls a cross-field pass to Devin who has set up behind a convoy running up the hash.

[Author note: This thing is long and pretty technical. That said, I think there will be sufficient payoff and value for you the reader. Still, be ye warned.]

Have you ever wished there were a convenient way to rate rushers the same way we rate passers? Sure, passer rating has its weaknesses—all mathematical formulas do—but despite it's issues, I've come to appreciate passer rating as a very useful framework to evaluate a player/team when it comes to passing the ball. In the same way that finding a corner piece to a jigsaw puzzle helps you figure out it's entire quadrant, once you have an idea of what to expect from the passing game you can leap to other touchstones to determine what to expect from the running game. A rusher rating would be just the sort of touchstone needed to really start messing around for those of us who are so inclined. This diary lays out what I think should work for these purposes.

To recap some of my previous work: passer rating combines four important factors—completion percentage, yards per attempt, interception rate, and touchdown rate—and blends them into one number. For rushing stats, important information for coming up with an analogous metric has been hard to come by until cfbstats.com came along. Tons of fascinating and useful data, for free. God bless the internet.

To come up with the rating, I looked only at positions that would be considered normal rushers (QB, RB, TB, FB, HB, SB, WR) that have an average YPC greater than zero. If you can’t meet those criterion, then you cant represent a normal rusher, thus sayeth the me. Other positions register rushing attempts but allowing the odd rush by a punter to color your view of what normal looks like would be dumb. See the chart below for more information. Also, if a guy averages negative YPC, uh, find something else to do, kthx. Other than that, no other filter was applied but some math wonk tricks were and I’ll talk about those as we go.

Parameter Mapping

Completion Percentage → Gain Percentage : Parsed play by play is necessary to generate a replacement for completion percentage. I opted to go for Gain Percentage: the percentage of attempts that resulted in more than zero yards. I figured the basic goal of a pass is to complete it (brilliant insight, I know) and the basic goal on a rush attempt is to gain positive yardage so…any gain of more than zero yards is mission accomplished. This parameter is as much about team skill as it is about player skill but the same can be said for Completion Percentage.

Yards Per Attempt: Direct analogue.

Interception Rate → Fumble Rate: The direct analogue would of course be fumbles lost per attempt but that’s not the right way to do it IMO. The luck factor that influences whether or not the team actually loses possession has nothing to do with the fact that bringing possession into question is a terrible idea. So, all fumbles whether lost or not are counted in the calculation.

There is also a bit of mathematical wonkiness deployed as well. Mike Hart is famous—at least around here—for his deftness at protecting the rock. It was awesome: 991 carries, 5 loose balls, 3 losses of possession. That was an aiight career, but these guys were kinda, sorta, maybe, better (!) at protecting the rock:

Player

Team

Att

FMB

Jacquizz Rodgers

Oregon State

789

1

Javon Ringer

MSU

843

3

Montee Ball

Wisconsin

924

4

OK, so the wonkiness…a lot of people who register meaningful rushing attempts do so at a pretty low level of opportunity. Even stud RBs often split carries with other backs: Eddie Lacy siphoned off carries from Mark Ingram before becoming the man, and T.J Yeldon did the same to Eddie Lacie. So in order for fumbles to make sense for players that get meaningful carries in low doses, we need to consider the question: at which point does a low fumble rate cross the threshold from wait-and-see to holy-crap-check-that-dude-for-stickem?

ABBACASTATS,BRUH!

What we have here is a chart comparing the observed percentage (red dots) and the mathematical probability (blue line) that a player will have at least 1 fumble versus the number of carries he has registered. The red dots are binned in increments of 1 so the sample sizes out past 150 are pretty thin but if bigger bins are used, you’d see a scatter of points that more closely follow the mathematical fit, because… math. The blue line was derived using logistic regression.

The weirdness at zero for the mathematical expectation might be concerning as it suggests that there’s a 20% chance you’ve fumbled despite not having a single carry to your credit. However, that is just an artifact of the data. It is possible to fumble on your one and only carry as actual observations show. What the math does, though, is it considers the sample size of the observations and then finds the best fit possible to the overall dataset. There are ways of dealing with that issue, but…I rather talk about football. Also, KISS. This is good enough for my intended purpose.

Anyway, the point of doing all that is it allows me to apply what I’ll call the Phantom Protocol. Basically, I take that curve, subtract it from 1, and add the resulting value to the player’s fumble total. As the number of carries increases, the effect of the phantom fumble recedes thus leveling the playing field and letting us evaluate players with low sample size as best we can. The result of this bit of data manipulation is that a guy with no fumbles in 16 carries is assigned an average fumble rate and by the time 100 carries are registered, the penalty is not perceivable. Below 16 carries, the assigned penalty is pretty stiff but this trick levels the playing field to let us look at guys with few carries and not just dismiss them with the low sample size red card. Sure, 16 carries is still a low sample but at least the rating self corrects for the fact that fumbles take time to manifest.

Most importantly though, the protocol adequately acknowledges players with low fumble rates even though they have a lot of carries. It’s easier to have a 1% fumble rate after 100 carries than it is to have the same rate after 789 carries. That said, after a while the fumble rates should be allowed to speak for themselves. Quizz Rodgers and Mike Hart need their proper allocation of DAP; nothing more, nothing less. I think the ghost protocol concept accomplishes exactly that.

Touchdown Rate: This one is also directly analogous but here again I’ve deployed the ghost protocol to credit guys with low sample the expectation of an eventual TD. TDs come about much more freely than fumbles do with goal line attempts and the like so this credit vanishes very quickly. But fair is fair: the protocol giveth and it taketh away.

Those are the components directly analogous to the ones used in passer rating and these would be enough to go about the business at hand. However, whereas a passer’s job is to get the ball into the hands of a play maker, players that are given the ball whether by pass of handoff are called upon to be the playmaker. Certainly the scheme, play call RPS, and execution of the supporting cast all have major influence on the results of a play but the ball carrier can do things that elevate the call from good to great. I wanted to be all formal-like and call this the Impact Run Rate but this [stuff] is s’posed to be fun, man. Hence—

Another Dimension: the Dilithium Quotient

The 20 yard threshold is usually referenced as registering a play as a big play. That would certainly qualify as a big play by any standard but that threshold seems to have been established somewhat arbitrarily in my opinion. On average, a generic runner on a generic team in a generic game gains about 4 yards per attempt with a standard deviation of about 7.5. Its called the standard deviation for a reason as a huge swath of observations (about 2/3rds) occur within 1 SD of the mean, or between –3 and +11 (remember: discrete data). The other 1/3 of observations get split evenly with 1/6 below -3 yards and 1/6 above 12. I’ve used objective criterion, you know, math, to define Impact Runs as those that register 12 yards or more. To register one of these the player’s entire team has to execute the play correctly, then the carrier he has to do something special (i.e. juke a dude, break a tackle, be fast). This is the real life manifestation of the Madden Circle Button and its informative. It’s the difference between Barry Sanders and Emmitt Smith.

Denard Robinson was great at this but it might be surprising to hear that he wasn’t the best. Percy Harvin in the spread option was ridiculous in this category. Percy had touched the ball a lot when he was a Gator and 27% of the time, he darted for an impact run. By Contrast, Denard’s DQ% was ‘only’ about 15%. Could you imagine Denard breaking loose almost twice as often? Of course, the scheme, the team’s execution of the scheme, and the player’s deployment within the scheme has a lot to do with this number. Florida circa Percy Harvin was galaxies away from Michigan circa Denard Robinson. Percy Harvin was the 3rd rushing option in Florida’s spread and shred, Denard Robinson was options 1-10. Also, being the QB in the spread-option means you are concern #1 for defenses: the cornerstone. That was triply the case when facing Michigan with Denard in the captain’s chair. Harvin was usually one-on-one with a guy 10 times slower than he was who was also probably pooping his pants.

Denard’s DQ% was pretty stable around 15% (scheme be damned) but his utility rate (723 career carries) was second to none save minor conference QBs. His closest proxy Pat White (684 career carries) broke loose at a 19% clip in RichRod’s Scheme. However, the Big EEEast sans Miami and Virginia Tech wasn’t quite the Big TEEEN. Denard went up against stout defenses way more often than Pat White did and did so without the benefit of Steve Slaton or Noel Devine and the benefit of a revolutionary offensive scheme. When Pat White lost RichRod is DQ% dropped to under 12%, Denard didn’t bat an eye. Everyone *knew* they had to stop Denard and only him on *every play* and they still had their hands full trying to actually do it. The fact that Michigan could never position itself for him to win the Heisman trophy will always be one of my sports fan laments. For ever and ever and ever. He better get a Legends Jersey or I’m qui’in’. I don't care if that’s silly. You’re silly. Where’s my bourbon?

Blending It All Together

Passer Rating was developed such that an average QB would end up with a rating of 100 according to the data set that was used to develop it, which was gathered two maybe three football eras ago when linemen couldn’t really block and scholarship limits weren’t so much. I’m not sure how they went about the process of pinning the rating to average==100 and I don’t have the data to try an replicate the results…so, I kinda, sorta, you know, pulled something outta my [hat]. That is to say: I did what I think is correct or at least valid. I normalized each parameter by it’s par value, summed them together, then forced resulting rating to equal 100. Ultimately the 100 thing is completely arbitrary, but negative numbers are weird, I guess. All said, a rating of 100 means the player was a solid runner but not special, below that you wonder if he should be running at all.

Where in the World is Carmen San Diego Mario Mendoza

Now that we have a calibrated formula its time to get down to business, application. I calibrated the rating so that 100 was a normal guy, but to figuring out what par should be is a little more complicated. I mentioned earlier that if you cant get to a rating of 100 I don't think you should be a primary running option and I also think we should only look at primary running options to establish our benchmark. But being a primary running option means different things depending on where you’re lining up.

When trying to crack a nut like this I often find that the data itself will help you figure out where to chop it. In the chart below I have plotted Average Rating vs. Amount of Carries. Obviously, the better runner you are, the more carries you should see but runners that are REALLY good are few and far between…this chart shows that dichotomy very nicely. I like to look for population gaps and/or inflection points in a performance curve. Those usually a good places to drop an anchor as far as I’m concerned. When they are near each other it’s a dead giveaway. Based on the data itself I’m using 115 for RBs, 70 for QBs, and 120 for WR as performance benchmarks.

Laugh Test

So, this is all well and good but the real test is whether or not things make sense. Here the values for the B1G in 2013:

Team Name

Player Name

RB Rat

Attempt

Yds/ATT

TD%

FMB%

Gain%

Dillitium%

OSU

C. Hyde

188.35

208

7.31

0.072

0.005

0.942

0.135

IND

T. Coleman

182.49

131

7.31

0.092

0.015

0.832

0.137

WISC

M. Gordon

172.71

206

7.81

0.058

0.015

0.888

0.150

WISC

J. White

169.86

221

6.53

0.059

0.000

0.810

0.122

IND

S.Houston

157.12

112

6.72

0.045

0.009

0.786

0.152

NW

T. Green

146.27

138

5.33

0.058

0.000

0.841

0.087

ILL

J.Ferguson

141.77

141

5.52

0.050

0.007

0.816

0.113

MSU

J. Langford

129.16

292

4.87

0.062

0.007

0.849

0.065

NEB

A. Abdullah

116.18

281

6.01

0.032

0.018

0.875

0.100

MICH

F. Toussaint

114.60

185

3.50

0.070

0.011

0.676

0.070

MINN

D. Cobb

112.55

237

5.07

0.030

0.008

0.827

0.084

PSU

Z. Zwinak

109.68

210

4.71

0.057

0.014

0.867

0.052

IOWA

M. Weisman

106.12

226

4.31

0.035

0.004

0.832

0.058

PSU

B. Belton

99.05

157

5.11

0.032

0.019

0.854

0.083

This generally looks pretty reasonable to me in terms of an overall ranking as well as a relative ranking. The players/team you’d expect to be at the top and bottom of the list are where they are supposed to be. If anything I’d criticize the Mendoza line at 115 given how we all feel about Michigan’s running game last year. Maybe 115 is just the threshold of suicide and 130 or better is what we fans really want from our teams. But, even this jibes with what I think.

As with passer rating, this rating depends on player skill, surrounding support, and offensive scheme. Toussaint’s YPC and Gain%—components heavily influenced by surrounding support (i.e. the O-Line)—are way under par. So is his Dilitium % which is a skill/talent/speed thing but the dude had a bum knee and he’s not that far off of par there. Makes sense. So, he hit the Mendoza line even though he had bad support in front of him, sorta like Gardner. These numbers make sense to me.

Re: Smith Vs. Green

I mentioned in my last diary that it was interesting to hear grumblings about De'Veon Smith being ahead/competitive with Derrick Green because I think the numbers bear this out. Check this out:

Player Name

Att

TD

Fum

Gain %

Yds/ATT

TD%

Fum%

DIL%

RB Rat

F. Toussaint

185

13

2

0.676

3.50

0.070

0.011

0.070

114.60

D. Green

83

2

0

0.723

3.25

0.025

0.004

0.048

83.42

D. Smith

26

0

0

0.769

4.50

0.015

0.023

0.077

73.05

These guys played with the same support and in the same system so the differentiators on display here are essentially Skill and Opportunity. Neither Green nor Smith actually registered a fumble but the Ghost Protocol affect Smith’s rating more because he has far fewer carries. Indeed, Smith’s rating is also bolstered by a phantom touchdown, but this effect dissipates faster because TDs occur more frequently. So the math is screwing Smith over here a bit. Meanwhile, Smith’s Gain % and YPC (hitting the right hole at the right time) and DIL% (juking, speed, whatever) were the highest on the team last season. Yep, Small samples yadda yadda. Just sayin’.

Anyway, that's a lot of words and I hope this was worth the read. Of course, I will be referring to this information in future diaries. Thanks for reading and let please provide and criticisms or comments you might have in, uh, the comments section.

I can't see where you’re comin' from / but I know just what you’re runnin' from / And what matters ain't the who's baddest / but the ones who stop you fallin' from your ladder.

For a little over four years now I’ve had a summer time hobby of trying to predict plausible performance levels from various QBs for the upcoming football season. I have tried to root these projections as deeply into the bedrock of reality as is possible for a figment of one’s imagination and at this point there is a codex of sorts in the diary archives describing my methods. It’s fun to go back and see what worked and learn from what didn’t.There’s something there, man.

For Devin Gardner 2013 I laid out two stat lines hinging on two sets of assumptions—a reasonable/prudent set, and a ‘sexy’ set. The reasonable prediction: Gardner would complete 225 of 360 passes for 2900 yards, 23 TDs, and 10 INTs. In reality he went 208 of 345 for 2960 yards, 21 TDs, and 11 INTs. There’s a HEAVY dose a good fortune involved there but, hot damn, that’s pretty good. The assumptions here were basically looking at only QB stats and nothing else Devin had shown enough in his 5 QB starts during the 2012 season to perform at the “seasoned veteran QB” level which I think of as an incumbent with 2 years of experience in tow. That's a brutal benchmark, IMO but that's what I measure guys up against. That's what we want them to be.

Anyway, the sexy set of assumptions were:

Devin has elite talent. I believe this one held. More on that later.

The O-line would be fine despite the possibility of being “a touch weaker than last year (2012).” Eh boy…

The offensive scheme would be well tailored to Gardner’s skill set and that of the support around him. This was sometimes true but not consistently often enough for Borges to keep his job.

Ok, so the necessary assumptions for DG to be the second coming of Vince Young vanished into the ether. But those last two assumptions about the support and scheme are really kind of baked into the reasonable prediction too. For my money, the fact that DG put up the numbers he was able to in spite of the glaring flaws of the team is a testament to just how good he can be if the conditions are reasonable.

The fact that there are so many straight-faced questions being asked about Devin Gardner’s incumbency status is ludicrous. Sure, numbers don’t tell the whole story but they tell a good part of it. DG went from being one of the darlings of the 2013 Manning Passing Academy to needing to prove his talent simply because he couldn't compensate for all of the flaws around him last season. He did as well as could reasonably be expected without adjusting for other very real headwinds.

I don’t practice Santeria. I ain’t got no crystal ball. I had a million dollars but I … I “spent” it all.

In an obscure part of Jim Mora's famous playoffs(?!?) presser, he gave the sports world the skinny on turnovers: "I don't care who you play--whether it be a high school team, a junior college team, a college team, much less an NFL team --when you turn the ball over 5 times...you ain't gon' beat anybody I just talked about. Anybody.” We all understand this via basic football intuition (ahem) but, stick around if you care to see if we can stick a number on that intuition.

Plenty of previous work on the subject has been done by many folks including myself. Football Study Hall recently conducted a study in similar fashion to how I’ve done it in the Blue Moon stuff and estimated the effect of per game turnover margin on season win percentage. FSH’s look lines up with the BMM, both suggesting that the gain on Season Win Percentage for per game Net TOM is about 100 basis points. The effect on overall record is useful but when watching a singular football game we’re not thinking about the whole season; we’re only thinking about the next few hours or so. How do the turnovers within a game affect the outcome of that specific game? To answer the question we’ll have to use math skills that go beyond grouping, counting, and arithmetic.

To answer the question at hand you need special math. In this situation you need to estimate probabilities because the outcome of a single football game is categorical (specifically binary) rather than discrete as in the case of full season wins. Herm Edwards gets it: “This is what’s great about sports …you play to win the game. [/Pitch Perfect Cumong, Man Glare]. Hellooo? You play, to win, The Game.” The point of sports is to beat Ohio State. Herm gets it. /Michigan orthodoxy

The special math is called Logistic Regression. It’s still a kind of linear regression but that regression is run through what is known as a link function to deal with the binary nature of the thing being modeled. This is done in all kinds of technical fields but for sports, um, investors this is a particularly nifty trick to have stashed next to your rabbit’s foot. The data for the model comes from NCAA.org as always. Sorry, no coefficients this time but I’ll show you a—

Here’s a useful way to think about this chart: suppose we were to play a Sunday morning game where I told you a team’s Final Turnover Margin and you had to tell me if they won the game or not—what would the payout odds need to look like for you to break even? This chart is the first step in answering that question.

Several features on this chart stand out to lend intuitive validity to the model. First there is neutral win probability at neutral TOM. Second, negative TOM hurts your odds, positive TOM helps them. Third, there are diminishing returns. By the time you get to +/- 3 in final TOM, the next turn over for/against you doesn’t affect win probability that much.

What’s Wrong, McFly?

Here’s the rub though, actually there are two rubs. First, that curve represents a generic team facing a generic opponent and neither of these things actually exist. I’ve used this example before but its worth a reprise: the generic US household has something like 2.4 children in it, but show me a household with 2.4 kids in it and I’ll show you a crime scene. Real football games are played by real football teams and they’re not all created equal. That curve shifts and bends according to the strengths of the teams in the contest. For reference, the math says “Nick Saban’s Alabama” can survive a –3 TOM against the nameless faceless generic team before it’s a coin flip situation. Let that sink in for a minute. Personally, I think that might be an underestimate.

So what’s the second rub? It’s related to the first one, actually. Here’s where our man Marty McFly comes in. I broke a major rule of predictive analytics to create this chart, I gave the model knowledge of the future. That’s a no-no for models that are supposed to be predictive because, duh. Don't give me that look, I told you I was a sinner last time. Deal with it. In addition to Final TOM and Game Outcome, I fed the model an end of season strength rating as well.

.

yes

.

That disclosure may spawn some skeptics and I welcome thoughtful discourse, but allow me to explain myself before you tar and feather me. I think we’re OK to do this for the specific goals at hand. Remember, the goal here isn’t to create a predictive model, it is to estimate as closely as possible the impact of Final Turnover Margin on Win probability. On the chart shown previously, you can’t make an evenly matched game a toss-up unless you know for certain that the teams are evenly matched, right? The final strength ratings serve as a discount mechanism to let the computer know “look man, we’re talking about Oregon vs. Colorado here…the Buffs are going to need a lot of help to have ANY shot.”

Here’s another and more specific example from the past but closer to home: Michigan vs. Toledo 2008. Going into the game, Michigan was a 17 point favorite. “This is Michigan vs. Toledo, fergodsakes.” Um, no, Biff, it was Michigan **2008** vs. Toldeo. If you had read the almanac you would know that Michigan 2008 couldn’t lay points on anybody. Why the hell did you risk your existence in space-time if you weren’t even going to read the damn thing?

So, now that we know what that chart is and what it cannot be, what does it tell us? Well, it says that turnovers are kind of a big deal, bro. How big a deal? The first extra possession is worth 16% in Win Probability. Basically, you’d need 2:1 odds in our little game to bet against the team with +1 TOM at the end of their game . In fact, in the generic case, its a simple equation: y = 2^x.

Sans The KNOWLEDGE, we would significantly under estimate the required odds by an increasing amount with each step away from neutral. Yes, I did the math the right way too, don’t worry about it, it’s irrelevant.

News Flash: we lack The KNOWLEDGE at several junctures. First the curve needs to be adjusted according to the true strength of both teams. You wont know how good each team actually is until they are done with their schedule—and maybe not even then—so, you’ll always have an error in your estimation for one or both teams. That error is lethal over the long run.

Second, and this is a biggie, you can’t consistently predict Final TOM. Both teams are in active competition to cause and avoid turnovers. Sure, if there’s a significant mismatch between the two teams, then you might be able to get a good guess in. But then, the end effect of turnovers go down as the rating gap increases so…well, let it suffice to say that there’s an error which is convoluted within an error.

Oy vey.

Taking Destiny by the Bit

Before I wrap this up, I need to talk about one more thing. TOM is one of those things, man. It’s out of your control. Try as they may, the defense can not expect to to get turnovers. They can try to provide the conditions necessary for turnovers to occur but they cannot make them happen. If a QB makes good decisions, no interceptions. If the ball carriers are Mike Harty, no fumble opportunities. Even if they aren't Mike Harty, you *might* be able to force fumble opportunities but you can’t guarantee a fumble recovery. You can try as hard as you can and still come up empty.

The offense however…seems like the offense can expect to not ever turn the ball over. Don't throw a pick, don’t drop a live ball, out scrap a guy for a loose live ball if you do lose your mind and drop it. You have agency in those things even if your opponent is trying as hard as they can.

So, screw TOM. Put the onus on the offense to not turn the ball over and then see what happens…Let me show you another—

I think this chart is astounding. Basically, it says that a generic team can cough the ball up twice to a par competitor and not hurt it’s win probability in any significant way. Eliminate turnovers completely (again, generic on generic) and you can lay 3:1. Cough it up once and lay 3:2 (ish). Actually, what this really says is that the typical team gives up two turnovers in a game against an equally matched opponent.

Interceptions are the Worst

This is bogue to QBs but the data don't lie:

Again this curve shifts and flexes depending on several factors but that’s the generic shape right there.If you’re up against a par opponent, your QB is “allowed” 1 mistake before he puts the team in a bad spot. Generic-vs-generic, the team that throws no INTs, wins 75% of the time. Which team will do that? What if they both do that?

Absent from this analysis is the timing of the turnover which is of course critical to its specific effect on the outcome of a game (Anthony Thomas fumble v. Northwestern). If that’s what you’re interested in, The Mathlete is your man.

I write this often because its important to remember: football is not a math test. Your game thesis could be dead to rights down to the weather forecast and you’ll still feel the break, feel the break, feeeel the break (/Santeria) very often. Often the decision comes down to believing in things you don't understand and/or can’t necessarily prove—not guilty and innocent are different things. Failure to reject the null hypothesis is not rejection of the alternate hypothesis. The rooting interest often defies logic and reasoning but that's what makes it so damn entertaining to have.

I confess: I care about the national championship more than orthodox Michigan dogma allows by, like, a lot. I think I understand what Bo may have meant; 1973 must have been a bitter pill to swallow. The split baby in ‘97 was crap, too. Actually bringing home the hardware often requires a bunch of rhetoric just to get the opportunity. Less so recently than in Bo’s day but there still some arguing that is needed. And, If people still need to be convinced with words that you’re worthy after all the games are over, how good are you really? Relying on the scruples of seemingly unscrupulous individuals is a bad idea so a “to hell with them” mentality is totally understandable. I believe that Coach Schembechler would have won one if he cared to do so. Define your own worth and all that but there is another way, isn’t there? Make it your goal to kick everyone’s ass and you’ll accomplish all viable goals anyway. Am I wrong? /WalterSobchack.

Times they are a changin’, amen, but I think the current system actually benefitted Michigan (and others) in many “just ‘cause” ways. For too many teams winning all of your games, though difficult and unlikely, still isn't enough to get a shot at a national title. Teams in the Hegemony don’t have to worry about that and can often afford to lose a game if its to the right opponent. The playoff system improves the situation but I suspect that there will still be some jostling to get a golden ticket. What does a team need to do to maximize its control of its own destiny?

To the Stat Cave!

The preceding chart is the basis of the revised Blue Moon Model, discussed and applied in my last diary. Hollow red circles (right axis) represent how many teams fell into each bin used in the lumping process. Scan this dusty diary for discussion of the lumping maneuver. The solid blue diamonds is the average win percentage (left axis) for each bin. See how the blue diamonds start getting out of line when the binned sample size drops into the muck? That’s the “low sample size” criticism we are all so familiar with. But note that it doesn’t take that many samples to get a reasonably well behaved average—getting 10 is usually enough but more is always better. The hollow blue triangles are the focus of this diary. Those are all of the teams that have played in the BCS title game since 2000. In terms of Net Yardage Differential, there is quite a spread ranging from 45 to 275 ypg.

In pre-school most of us learned that a good way to identify differences is to ask ourselves “which of these things are not like the others?” We also learned to mock the weirdoes because why else would you go through the trouble of identifying them in the first place? The preceding charts make the MNC and B1G contending weirdoes stand out pretty well. Go forth and mock them.

Championship game losers are included because they had destiny by the bit. The mountains meet the sea at least 130. The typical team showed up with a yardage differential of about 165.

In the B1G, the Mendoza line is 100 ypg in yardage differential. What’s interesting to me is that it looks like there was a sea change in the B1G around 2005 with most of the teams underwater showing up before then. The only Michigan team that could pass muster as a national champion is the 2003 squad. Season summary:

Lost @ Oregon by 4 with a net turnover margin of –3; ‘nuff said.

Lost @ Iowa by 3 with a net turnover margin of 0 but: punted from the Iowa 35 in the first quarter, failed to score a TD after 1st @ Goal from the Iowa 8 in the second quarter, had a punt blocked in the third quarter leading to a 3 and out FG for the Hawkeyes.

Ah, memories / light the corners of my mind / misty watercolor memories / of the way we were … Memories / may be beautiful an yet / what’s too painful to remember / we simply choose to forget. Tell ‘em, Babs.

With the playoff system coming winning a National Championship is now harder for most. By most I mean everybody except for Nick Saban. Saban built a monster at LSU, handed off to Les Miles to terrorize for a while until he could get all three rings installed for his clown show, then came back to build an even badder monster at Alabama. Now Saban can be ranked as low as 5 and have the rhetorical juice to jump some teams. If he gets in, chances are, he will win. ‘Sall good though, dude is mortal…right? Um…maybe Kirby Smart is the real master mind??? Oh shit, do they have a succession plan in place?!? Madre de dios!!!

(Sorry about that. I actually think I’m doing well this time.)

Sure, Justice will be served more often because being undefeated and winning 1 game you have 6 weeks to prepare for aren’t enough to claim the throne anymore; now you have to beat two more teams that are pretty damn good and doing that is, like, hard. I support the cause for sure, but I recognize how much steeper that mountain is about to get. But, but, but, over signing! Look, man, you can’t blame a shark for being the baddest mofo in the ocean; you’ve got to blame the ocean. The whole over signing phenomenon that we’re seeing in the SEC may be distasteful to some but so are fried insects. I’ll walk this back: scout’s honor.

That’s a real picture of what is considered yummy street food in Cambodia.Also, I once ate Chapulines on a business trip to Mexico because I knew I wouldn’t die and it was a reeeally nice restaurant and I didn’t order it and “we’re closing this deal one way or the other, fellas” and “Yes, I’m this crazy, ese”. Also: tequila and yolo. Chapulines are fried grasshoppers and they don’t taste bad (wouldn’t say they taste good either) but its the texture and mouth feel that get you. Crunchy, gritty, all the moisture in your mouth runs for the hills…just all around nasty. I stayed cool and chewed them like a man but at a certain point I was all like “fuck this I’m swallowing” just to get that shit out of my mouth in the least ridiculous way possible and, after all, I had done this to my self. The legs, man, the legs. Those tiny little barbs aren’t meant for swallowing. Don’t judge me, man, I have lived. Thank God for Dos Equis.

Football, yes. Check it out: obviously, over signing isn’t against the rules. We don’t have to like it and we don't have to do it, but if you can choke it down -- or maybe you think its yummy with barbeque sauce -- NCAA enforcement says “bon appetit. Besides, top notch recruiting isn’t enough -- we know that first hand here at Michigan (also see Les Miles) -- you need to turn those recruits into great players and support them with great game plans and great play calling and great-never-ever-punting-on-the-opponent’s-35. Take away over signing from Alabama and Saban & Co will still kick everyone’s ass, probably.

To be a team the can get to the playoff and win the damn thing -- to be a champion’s champion -- you need to be “free to roam the plains, your majestic rippling muscles trampling over mascots that dare oppose you” as Brian once said. You must leave a barbaric path of destruction in your wake. You must leave only the lamentations of their bloggers to tell your tale. Being above 130 in NYDS since 2000 has meant you were, on average, in the top 8 nationally in that category.

There is a limit to how good your defense can be: the number 1 team in NYDS averages out with a Defensive YPG of about 270. The best team in the country in DYDS averages 230 ypg allowed. The best defense I have on record is Alabama 2011 at 183 (jeepers). Seriously, there are typically 13-ish possessions per game so you could score at TD every time and allow 107 ypg and still not get cored on. I guess you could try for onside kicks too but … what an asshole! The typical team “worthy” of a national championship has a defense that yields 315 ypg.

If you read my last entry, you should be encouraged to see that championship defense isn’t much better than what Brady Hoke and Greg Mattison have been able to field since their arrival on campus, even with all those roster issues we’ve come to know so well. Today, the roster is better and improving. It’s not easy to have great defense, but these guys know what they’re doing.

The mantra of defense wins championships isn’t [baloney], it’s just not enough. See, defensive performance is so hard to predict. With offense you have a reasonable shot at summing up the parts to get to OYDS. No such luck on defense. Proxy analysis can give us an idea of how good our defense could be but you will never know what you’ve got until you see it perform.

Football is not a math test.

I think I can see how Michigan can get to 100 NYPG this year….130 isn’t that far away.

Everytime the moon shines I become alive. I’m a beast in the night. I’m on the prowl and I hope to find some light.

-Kid Cudi, Alive (Nightmare)

We all have our on favorite canons, don't we? Once upon a diary in a dark and distant era I made reference to one of mine, that one by Pachelbel; You know the one. It is inescapable. I mean, et tu Rap? Eminem, Jay Z, Tupac. But more favorite to me than the original is Blues Traveler’s riff on it, Hook. The song itself is kind of a slap in the face to the general public who’s taste in music is, apparently, so trite and unsophisticated that we don't realize that “when I’m stuck and need a buck, I don’t rely on luck” meaning musicians can just hijack Pachelbel and we’ll gobble it up anyway, even if we can’t put a finger on just why we like it.

“Hey pizza guy! Dough, sauce, cheese, and toppings? What kind of cookie cutter bullshit is this???” Relax, Popper, that [stuff] is delicious so what exactly is the problem here?

(Unfortunately for you all the combination of deep night, data induced madness, and alcohol overcomes the “this only fascinates me” levees in my mind and the inmates overrun the asylum so all apologies and thank you for indulging me.)

So why do I like the song even though Popper is being kind of a dick? Because it’s a brilliant monument of knowledge, understanding, wit, and self awareness. And irony. The song is a musical trope wrapped around lyrics consisting of lyrical analysis, mocks its performers, its industry, and its audience and STILL made hella loot. Kharma(n)* is a bitch though: Blues Traveler hasn’t had a song that popular ever since and it was this close to being their most popular single ever.If Penn & Teller did music, they would would do this.

For me, peeking behind the curtain piques my curiosity rather than diminishes my interest. It makes me think “hey, I can do that” for a couple days until I remember “oh yeah, I lack skillz and talent.” And then I settle into true appreciation and fascination in watching people do things I cannot. The hook brings you back, man. Again and again.

My point in all that was that worthy synthesis is derivative of rigorous analysis. That is, a good place to begin creating something worthy of creation is by understanding things that are worthy of understandation. Follow? UFR, Mathletics, other stuff, they’re all the same—they break things down in order to build other things up. Even if the thing that is being built up is just context, that’s a thing worth building. If you know a bit about something and look at it upside down, sideways, and in a mirror, you might just see something kind of cool…in a nerdy sort of way. ‘Tis the Canon of MGoBlog.

Okay, dayenu.

*MCalidagger said “that’s Carmen!” to Lady of the Lake one time when he was three just seconds after she had stubbed her toe while placing him in the purgatory of time-out. He was mad, she was mad, it was hilarious. Then I got in trouble for laughing. How is that my fault? Troof is troof, yo.

Here We Glow Again

Another of my favorite cannons (remember: gun == Multivariate Least Squares Linear Regression Model) is what I call the Blue Moon Model. It really began as a basic assessment technique with which to project my team’s prospects with a few simple lower level assumptions. Three years on, I think its worth a remix. Previous foundations are laid here and here.

As a refresh the model takes a team’s Offensive Yards per Game, Defensive Yards per Game, and Turnover Margin per Game and converts that to an expected Win Percentage. IT IS A RETRODICTIVE MODEL so, it’s predictive value relies upon the validity and accuracy of the assumptions that are made. Even when you’re dead nuts on with those assumptions, you’ll be off by more than 1 win about 26% of the time. So, good luck guessing, then good luck winning. That’s the betrayal part of the name Belewe. This is not a problem though, in the world of inductive logic even though conclusions are only probable, they are useful nonetheless. Also, being able to lay 3 to 1 odds is pretty good. And, guessing aint that hard when you know what you’re doing (upside down, sideways, mirrors…did I mention incense?).

It turns out, the model can be boiled down even further without sacrificing it’s accuracy by collapsing OYDS and DYDS into Net Yards per Game (NYDS). Voila, a 2 factor model with killer statistical significance. The intercept makes more sense too because it is unbiased. Say your team is average (OYDS = 375, DYD = 375, TOM = 0); why should you expect to win an extra 3% of your games? Trick question; You shouldn’t. The best application of this math is to make your assumptions about offense and defense, turn them into an average yardage differential, set TOM to 0* and proceed with your projection.

*For the last time (yeah, right) predicting TOM is a fool’s errand and that's coming from a guy that LOVES trying to predict stuff. Go ahead and try but you’re wasting precious time that could be used to make more worthwhile assumptions.

3 Factor Model

2 Factor Model

Model R-Square

0.7619

0.7617

Intercept

0.5319

0.4995

OYDS

0.0018

x

DYDS

-0.0019

x

NYDS

x

0.0019

TOM

0.1078

0.1082

I know this model is simple but that’s part of it charm: you can do this math in your head. Take your yardage differential, round by 5, divide by 5, move the decimal two spots to the left, add 50% and ADJUST BY 10 PERCENT (will never get over that) for each net turnover. I appreciate the sophistication of college level analysis but I was way smarter in elementary school. Arithmetic is where its at, homies.

I think there are two main applications of the model: expectation setting and benchmarking. This diary is long to I’ll split the benchmarking bit off into a different diary.

Expectation Setting

All fans want to know the same thing: how good are we going to be this year? Sensibly, we start at the end of last year then plug any holes left behind by attrition and arrive at an expectation of X because, naturally. I have no beef with that process because its a whole lot of fun, but you need to have the right starting point. BMM is handy for this. Here’s how local schools of interest did last year:

Team

OYDS

DYDS

TOM

NYDS

2012 Wins

BMM Expect

Delta Wins

Notre Dame

412.2

305.5

0.62

106.7

12

10.0

2

Ohio St.

423.8

359.6

0.25

64.2

12

7.8

4

Nebraska

460.8

360.6

-0.86

100.2

10

8.3

2

Northwestern

394.6

378.2

1.08

16.5

10

8.4

2

Wisconsin

393.3

322.6

0.21

70.7

8

9.2

-1

Penn St.

417.5

353.4

0.75

64.1

8

8.4

0

Michigan

383.1

320.0

-0.69

63.1

8

7.1

1

Michigan St.

359.3

274.4

0.15

84.9

7

8.8

-2

Purdue

402.7

416.2

-0.15

-13.5

6

6.0

0

Minnesota

321.4

358.6

-0.15

-37.2

6

5.4

1

Indiana

442.0

463.5

-0.25

-21.5

4

5.2

-1

Iowa

310.4

381.6

1.00

-71.2

4

5.7

-2

Illinois

296.7

387.6

-1.00

-90.9

2

2.6

-1

This year’s prize for Most Dissonant Record goes to: Ohio State. Plus 4, folks. Thirteen years of data has only seen that feat accomplished 8 times out of over 1500 total observations. Fun Fact: that is the third time OSU has managed to post a +4 during that period: 2002, 2003, 2012. In 2004 they posted a +3 followed by +2 in 2005 and 2006…wtf, man? Tresselball, that’s wtf. Ball Control offense, good to great defense, low risk play calling. Jim Tressel hates math, Q.E.D.

I submit that the extended deviation is the offense’s “fault” because when you have good/great defense, you generate yardage differential by racking up yards on offense. What are you going to do, allow 0 yards per game? So I think the Tresselball offensive philosophy explains why Ohio State consistently defied the math for so long. Once the Buckeyes started stock piling national level talent and opened up their offense to leverage it a little more, their performance lined up with the model just fine. Until last year.

Look, I expect Ohio State to be a formidable opponent as usual but, #2 in the country they ain’t; at least not right now. Well, they are a deuce just a different kind of deuce, nameen? Shout out, to my local head start program. Anyway, Urban Meyer’s Florida teams leveled off at 450 ypg and I think OSU offense will be there this year. Yeah, yeah ESS EEE SEE defenses (!) but OSU’s roster isn’t Florida circa 2008 either. The typical B1G defense is OK all things considered, not great but not necessarily a pushover either. Braxton Miller is good but he has some work to do in his passing game, I need to see it first. I’m sticking at 450 OYDS. 475 is on the table but, show me.

Defensively, nothing has really changed at Ohio State. Coaches, recruiting, philosophy, nothing. Well, tatgate happened. During the tatgate era Ohio State’s defense was insane: 300 DYDS or better, often much better (275 or lower), every year between 2005 and 2010. Then, oops, back to typical (about 325 for them). I hereby grant them reasonable improvement on defense from last year out of the goodness of my heart and they end up at NYDS = 100. That’s 9 wins, with a shot at 10. I’d hate to see them get unlucky, truly.

Meanwhile, in Michigan

I’ll take the more straight forward part first, the defense. Not that its clear or easy just that, because of the reactive nature of defense, I think the best policy is to look at a program’s track record, give consideration to any systemic and roster issues that might exist, and call it a verse.

Rich Rodriguez era notwithstanding, Michigan’s Defense has been pretty consistent by the singular measure of DYDS. With competent coaching and a Michigan caliber roster, we typically hang out in the 300 – 350 zone; last couple of years we were at 325. Now Greg Mattison is pretty good but to start breaking through to the next level of defensive prowess and start heading toward elite, I think Michigan needs more experience and maybe a touch more raw talent. Jordan Kovacs will be missed but Heininger Certainty Principal, jack. I’m sticking with a base expectation of 350 – 325 for that side of the ball. Anything better than that would be kind of amazing.

Offense is trickier, especially with the loss of Darboh. Its no revelation to say that Michigan’s offense should take a step forward this year with more harmony between conductor and orchestra so it’s correct to expect more than average OYDS this year, but how much more? Since 2000, Al Borges has never called an offense better than about 425 (Auburn 2004). Indiana put up a 450-ish in the B1G last year so it’s possible and Michigan has better talent on its roster right this minute than Indiana does, but I don’t think we have an offensive philosophy like Kevin Wilson’s either. And we don't have the talent / experience overall to simply out-execute everybody like Alabama does(450 last year). Let’s build it up from one more level down just to make sure.

I’m on record for Devin to pass for 225 to 250 ypg this season. The loss of Darboh gives pause, but I’m not backing off on that. So, getting to 425 means we need 175-200 ypg rushing from the backfield. That’s where we were last year with Denard featuring heavily in the run game. Fitz was a different back seemingly reverting back to 2011 form with Devin under center but then there was that leg thing. I’m going to forget about the leg thing and the questions re: the interior line (thou shalt not accuse me of not being generous) and give Fitz 1000 yds on the year leaving about a 100 - 125 ypg gap to get to the desired rushing target. Y’all think I’m crazy but I think we need to get about 50 ypg out of Devin on the ground to get to an offensive performance level that will keep us from freaking out unless one of the other RBs emerge to provide 600 – 700 yards on the year.

I can’t convince myself to go over Auburn 2004 and that’s being generous. What’s the Borges version of HCP? Even without the questions vis-a-vis the running game, going over 425 probably demands Devin the Monster and a Adrian Peterson level recovery from Fitz and Al Borges’s best offense ever. Again, things happen but I’d be kind of amazed if that happened. You can’t outrun your canons; you acquire new ones. That’s possible, but humans are some stubborn mofos. 400 – 425.

BMM says: 8 or 9 wins with a shot to win 10. If you think Michigan can get to a TOM of +0.7, shift your expectations up by 1 win, then go take your meds.

The Road to Indy

Legends division looks pretty tough this year. Nebraska has a lot coming back and has TOM mean reversion working for them. MSU got unlucky in close games and stands to see at least modest improvement on offense to compliment an elite defense returning virtually intact. Northwestern doesn’t really look as good as last year’s record to me and they had a nice TOM working for them last year; they’re on reversion watch. And their schedule is brutal. Still, the Wildcats are pesky.

The two most important games on the schedule occur November 2 and 9 (duh). Win both and we’re probably in the B1G title game. If we split those, we’re likely to be in a tie with Michigan State or Nebraska possibly both going into The Game which we will have to go all out to win.